Building the Science of
AI Governance
Research-backed frameworks and methodologies for enterprise AI compliance, governance, and risk management. Built from real-world implementation experience.
Research Contributions
Frameworks and methodologies developed through years of enterprise AI implementation. Each addresses critical gaps in traditional approaches to AI governance.
Precision Drift Detector
Key Contributions
Cognitive Systems Management (CSM)
Key Contributions
Red Audit Kit
Key Contributions
LegacyShift Methodology
Key Contributions
Active Research Areas
AI Regulatory Compliance
- EU AI Act implementation strategies
- Cross-jurisdiction compliance frameworks
- Automated compliance monitoring
- Policy-to-implementation mapping
Enterprise AI Governance
- Multi-model governance at scale
- Organizational governance structures
- Stakeholder alignment frameworks
- Governance automation
AI Risk Management
- Silent failure detection
- Cascading risk analysis
- Risk quantification methodologies
- Real-time risk monitoring
System Modernization
- Legacy AI migration patterns
- Technical debt assessment
- Modernization without disruption
- Compliance-preserving refactoring
Published Work
The Instruction Stack Audit Framework (ISAF): A Technical Methodology for Tracing AI Accountability Across Nine Abstraction Layers
Addresses AI accountability failures by providing a nine-layer technical specification for documenting instruction propagation from hardware to outputs. Includes 127-checkpoint audit protocol, cryptographic verification, and risk scoring based on abstraction distance. Demonstrates application to EU AI Act, NIST AI RMF, and ISO/IEC 42001 compliance requirements.
view paper →Deterministic Bias Detection for NYC Local Law 144: Why Reproducibility Matters More Than Accuracy
Presents a reproducibility-first architecture for detecting linguistic bias in job descriptions under NYC Local Law 144. Argues that regulatory compliance requires deterministic systems over probabilistic ML models. Details rule-based pattern matching, version-controlled lexicons, reproducible scoring, and cryptographic evidence generation for legally defensible documentation.
view paper →From Industrial Electrification to Artificial Intelligence: Institutional Lessons from Construction Governance for AI Risk Regulation
Analyzes the institutional evolution of construction governance and applies its structural lessons to AI risk regulation. Draws from historical developments in mechanization, electrification, occupational safety regulation, professional licensing, and insurance enforcement to propose a phased governance maturation model for AI systems.
view paper →From AI Pilots to Regulatory Readiness
Practical framework for transitioning from AI experimentation to production-grade, compliant systems.
view paper →Why Enterprise AI Integration Strategies Fail
Systematic analysis of common architectural and organizational failures in enterprise AI adoption.
view paper →Cognitive System Management: A Framework for Enterprise AI Project Governance
Original publication of the Cognitive Systems Management (CSM) framework: a four-domain governance methodology comprising Enterprise, Project, Code and UX. Current version: CSM 2.0 (spec v2.0.0, 2026-08-10).
view paper →Compliance Law Guides
These research frameworks inform practical compliance guides for the AI regulations that matter most.